CN109685237A - A kind of real-time planing method of unmanned aerial vehicle flight path based on the path Dubins and branch and bound - Google Patents
A kind of real-time planing method of unmanned aerial vehicle flight path based on the path Dubins and branch and bound Download PDFInfo
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Abstract
The present invention relates to a kind of real-time planing methods of the unmanned aerial vehicle flight path based on the path Dubins and branch and bound, belong to technical field of route autonomous planning.The present invention combines in the path Dubins with Branch-and-Bound Algorithm, for planning that unmanned plane in plane avoids the most short track of all obstacles in real time.Kinematical constraint of the track based on the path Dubins due to considering fixed-wing unmanned plane, is more in line with the flight characteristics of unmanned plane.Meanwhile the present invention, using Branch-and-Bound Algorithm, not only reduces storage consumption in the extension and ergodic process of track search tree, but also improves track search traversal of tree speed.Compared to conventional method, the present invention has carried out the achievable simplification of engineering to trajectory planning problem, has taken into account flight path quality and planning speed, and engineering practicability is strong.
Description
1. technical field
The invention belongs to unmanned aerial vehicle flight path contexture by self fields, and in particular to a kind of reality of fixed-wing unmanned plane overall situation track
When planing method.
2. technical background
It is improved as UAV Intelligent demand is promoted with hardware processing capability, the online trajectory planning of unmanned plane has become
Trend of the times, it is necessary to find a kind of high reliablity, strong real-time, the Path Planning that storage consumption is low, engineering practicability is strong.
Existing unmanned aerial vehicle flight path planing method has two: first is that the track that existing path planning method obtains is often by a system
Column track points composition, and unmanned plane is limited by maneuverability, has certain turning radius, the track of this form in turning
And the flight characteristics of unmanned plane is not met, need subsequent track optimizing；Second is that the complexity and storage of unmanned aerial vehicle flight path planning
With problem scale, exponentially type increases for consumption, and unmanned aerial vehicle onboard computing resource is limited, and existing path planning method is difficult to reality
It is planned when real, engineering practicability is poor.
3. summary of the invention
For overcome the deficiencies in the prior art, in view of the above-mentioned problems, the present invention provides one kind based on the path Dubins and
The real-time planing method of the unmanned aerial vehicle flight path of branch and bound.
The technical scheme adopted by the invention is that: by the path Dubins in conjunction with Branch-and-Bound Algorithm, for solving plane
Interior unmanned plane avoids the most short track of all obstacles.Planar, there are maximum curvature limitation in the case where, give starting point and
The position and speed direction of target point, the path Dubins be by starting point reach target point shortest path (shortest path
Existence is proved by Dubins earliest).The path Dubins is only made of arc section and straightway track, due to considering fixed-wing
The kinematical constraint of unmanned plane, the track based on the path Dubins are more in line with the flight characteristics of unmanned plane.In track search tree
Extension and ergodic process in, using Branch-and-Bound Algorithm, trim track search tree, not only reduce storage consumption, but also improve boat
Mark searches for traversal of tree speed.Simultaneously on track barrier-avoiding method, using the method for making geometry tangent line, both make the boat cooked up
Mark meets the requirement in the path Dubins, and reduces operand.
When illustrating the process of trajectory planning, present invention uses some concepts with specific content and meaning, explain
It is as follows:
1) obstacle, obstacle circle and obstacle group
Constraint of the unmanned plane in flight course by bulk properties constraint and external environment.In two-dimensional surface, for
Bulk properties constrain us and only consider that the minimum turning radius as caused by side acceleration limitation constrains, this constrains in track
On show as track radius of curvature and must not drop below minimum turning radius.External environment constraint is main to consider terrain obstruction, meteorology
Threat and no-fly zone etc..The present invention is unified that they are embodied in independent circle or a group one by one when handling these constraints
It is handled in group's continuous circle interconnected.External environment constrains on two-dimensional surface show as to leap, certain shape
The plane domain of shape, these plane domains can be with single circles or a series of circles interconnected come approximate representation.This hair
Bright in order to keep track most short, all tracks are close to these circular boundaries, as long as so guaranteeing that the radius of these circles is not small
In the minimum turning radius of unmanned plane, so that it may meet drone body performance constraints.Therefore, for sake of convenience, default turning
Radius Constraint has met, and does not consider further that.
As shown in Figure 1, the figure is the digital elevation map in a certain area.Since unmanned plane is in the overwhelming majority of the task of execution
Time is in cruising condition, and flying height substantially remains in cruising altitude, can intercept in cruising altitude to hypsographic map,
Obtain the two-dimensional map of the height, by trajectory planning problem by Simplified Three-dimensional be two dimension.The two-dimensional map being truncated to, which has, does not advise
Boundary then, this map are not easy to store, and are also unfavorable for subsequent avoidance processing, are pre-processed.Preprocess method
Are as follows: by a series of round approximate representation of the terrain obstruction in two-dimensional map.As shown in Fig. 2, the figure is to Fig. 1 map a certain
The approximate map that height is intercepted, and obtained after being handled.Landform boundary institute enclosing region in Fig. 2 is individually justified,
Or a group circle interconnected replaces, while no-fly zone and meteorological threaten usually have certain operating radius, can also see
The circle for making certain radius size is added in the map.The map is exactly map used in subsequent progress trajectory planning, herein
Agreement: the obstacle circle mentioned later refers to single, independent obstacle circle, and obstacle group refers to a group obstacle circle interconnected,
Obstacle is the general designation to obstacle circle and obstacle group.
2) track search tree
The target of the method for the invention be to solve for it is most short can flight mark (can fly that track is required to meet two o'clock: avoid owning
Obstacle and meet minimum turning radius constraint), i.e., most it is short can flight mark be optimal solution.Each connects starting point and target point
Track is likely to be required optimal solution, we are called may solution.All possibility solutions form solution space, and actually solution is empty
Between the number of middle possible solution be infinite multiple, but do not need to traverse entire solution space to find optimal solution, not need more
Establish entire solution space.The present invention carries out the search of optimal solution by building track search tree, and track search tree is solution space
Subset, only scanned on track search tree, the optimal solution one found is positioned in track search tree.Track search tree
It is not disposably to construct, is then traversed again, building, trimming and the traversal of track search tree is alternately.
3) live node
Each node on track search tree indicates the track of connection a starting point and target point.Search tree slip-knot
Point refers to the node not being traversed on track search tree, and there are two types of sources for search tree live node: first is that setting when search tree initialization
The root node set；Second is that newly expanding the node for coming but not being traversed.Why search tree live node is referred to as live node, is
Because this node may be that optimal solution or optimal solution there may be present in the subtree of this node, live node has
It may be extended.
4) fast knot point
The node being traversed can all become fast knot point, particularly may be divided into situation in three.The first is: the node is not most
Excellent solution, and optimal solution can not be present in the subtree of the node.Second is: track represented by the node is shorter in length than
It is current it is optimal can flight track, and avoid all obstacles.The third is: the node propagated through.Search tree fast knot point is not
Expansible, this, which is equivalent to, cuts down track search tree, reduces the scale of track search tree.
5) track node
Trajectory planning is carried out, first having to determine how indicates a track.One complete track is usually by a series of
Discrete track points indicate that the quantity of track points is more, and track obtained is more accurate, corresponding to need to pay bigger deposit
It stores up space and calculates cost.The present invention be directed to the path Dubins the characteristics of, using be different from by the way of discrete track points come
Indicate a complete track.Based on the path Dubins, all tracks in the present invention are turned by radius greater than unmanned plane minimum
The constant curvature arc section or straightway track of radius form.If a track one based on the path Dubins shares n sections of circular arcs
Section track, so that it may indicate a track with n track node.One can be completely represented with such n node to be based on
The track in the path Dubins, and it is different from the track indicated with discrete track points, the track indicated with node is theoretically
It is unlimited accurate, and indicate that storage size required for a track is only related with the quantity of circular arc in track.(note:
Track node is to be different concept with the node of search tree for describing track)
6) it is current it is optimal can flight mark
It is current it is optimal can flight mark refer to that during pressing progress trajectory planning of the present invention, current time is found
Length it is most short, avoid the track of all obstacles.In the initialization of track search tree, any boat of flying is not found
Mark, our one length of imagination are infinitely great track at this time, agreement as it is initial it is current it is optimal can flight track.
7) limit
In the ergodic process of track search tree, we use Branch-and-Bound Algorithm, and wherein limit is used to cut down search empty
Between, limit the scale of search tree, and it is current it is optimal can the length of flight mark just play the role of limit in this process.Boat
Each node on mark search tree indicates a track based on the path Dubins, when traversing each node, needs to judge
The node is live node or fast knot point, and the foundation of judgement is: the length of track represented by the node flies with currently optimal
The size relation of track length.If the length of track represented by the node be longer than it is current it is optimal can flight mark length, the knot
Node in point and its subtree can not be centainly optimal solution, so the node is judged as fast knot point.Due to it is current it is optimal can
The presence of flight mark, a part of node on track search tree can be judged as fast knot point, therefore limit the scale of search tree,
Reduce search space.
8) branch
Branch is exactly the process of track search tree building extension.For the live node on search tree, need to determine its generation
Whether the track of table has bypassed all obstacles: if so, the track become it is current it is optimal can flight mark；If it is not, the then track
Several obstacles have been likely to be encountered, therefrom an obstacle have been selected to be handled according to certain strategy, i.e., around selected obstacle.
On two-dimensional surface, there is both direction clockwise and anticlockwise around an obstacle.The result of branch shows track search tree
Upper is exactly to be extended to currently processed node.
Illustrate ramifying by an example.As shown in figure 3, lower left corner triangle position is specified starting
Point, the five-pointed star in the upper right corner are specified target point.Initial track representated by root node on track search tree are as follows: connect
The straightway track of initial point and target point.This track is collided with the obstacle group in map, needs to carry out branch.Such as figure
Shown in 4, two new tracks for bypassing obstacle group, the method for generating new track can produce on the basis of former straightway track
To make geometry tangent line cut-through, when it is to be processed be single obstacle bowlder, the practice of geometry tangent line is fairly simple, but when wanting
When that handle is the obstacle group of syntagmatic complexity, the practice of geometry tangent line is complex, and this example shows only a simple feelings
Shape.Obtaining this two tracks is the path Dubins being only made of straightway and circular arc, extends to obtain corresponding to from root node
Two new nodes.
Compared with prior art, the present invention is obtained has the beneficial effect that firstly, the track that the present invention plans is not
It is made of a series of track points, but is made of series of arc and straightway, more meet the flight characteristics of fixed-wing unmanned plane；
Secondly, the present invention uses Branch-and-Bound Algorithm, it is trimmed while establishing binary search tree, greatly reduces search
Space, while the method for making geometry tangent line is used on track barrier-avoiding method, operand is reduced, planning speed is improved,
Ensure real-time, engineering practicability is strong.
4. Detailed description of the invention
Fig. 1 are as follows: somewhere digital elevation map
Fig. 2 are as follows: the interception of Fig. 1 a certain height and treated approximate map
Fig. 3 are as follows: track and an obstacle group collide
Fig. 4 are as follows: around two paths Dubins of an obstacle group
Fig. 5 are as follows: branch and And Bound Algorithm flow chart based on the path Dubins
Fig. 6 are as follows: trajectory planning problem-instance
Fig. 7 are as follows: in example solution procedure, handle node n1
Fig. 8 are as follows: in example solution procedure, handle node n2
Fig. 9 are as follows: in example solution procedure, handle node n5
Figure 10 are as follows: in example solution procedure, handle node n3
Figure 11 are as follows: in example solution procedure, handle node n4
Figure 12 are as follows: the final program results that example solves
5. specific embodiment
It is described further below by an example in conjunction with attached drawing in order to illustrate planning process of the invention.By
In using the present invention carry out trajectory planning complexity, it is unrelated with the absolute size of mission area, thus the example be indifferent to away from
From absolute size, unit takes 1.Trajectory planning problem to be processed is as shown in fig. 6, the point S that the lower left corner is marked with small triangle
For unmanned plane initial position, the point G that the upper right corner is marked with small five-pointed star is unmanned plane target position, and the obstacle in map only has 3
A obstacle circle, is respectively labeled as 1,2,3.
Track search tree is initialized first, and root node n is set1, while arrange it is current it is optimal can flight mark length be infinite
Greatly.Only contain root node n in track search tree at this time1, and without completing traversal, root node is handled.Arrange herein, it is attached
In the track search tree of Fig. 7, an embedded square circle indicates the node being presently processing, and circle expression is not traversed
The node crossed, i.e. live node indicate fast knot point by the circle for drawing a upper fork.Solid line track in Fig. 7 is currently processed knot
The track that point, i.e. root node represent, the track length are 141.42, are less than infinity.Next judge its whether in map
Obstacle collide, as a result the track and obstacle circle 1 collides, and followed by branch, generates the two of cut-through circle 1
The new track of item, as shown in two dotted lines in Fig. 7.Accordingly in track search tree, by root node n12 new knots are expanded
Point n2And n3。
Judge whether track search tree completes traversal again, there are two node ns2And n3It is not traversed to.Selection wherein one
It is a to be handled, as shown in figure 8, selection node n2It is handled, node n2Corresponding to the solid line track in figure, the length of the track
Degree is 142.85, is less than infinity.Next judge whether it is collided with the obstacle in map, as a result the track and barrier
Circle 2 is hindered to be collided.Likewise, obtaining two new tracks shown in dotted lines in Figure 8 followed by branch, existing accordingly
Again from node n in track search tree2Expand two new node ns4And n5。
Judge whether track search tree completes traversal again, there are three node ns4、n5And n6It is not traversed to, therefrom
Selection one is handled.As shown in figure 9, selection node n5It is handled.Node n5Corresponding to the solid line track in Fig. 9, the boat
Mark length is 146.31, is less than infinity, and the track avoids all obstacles in map, so the track becomes newly
It is current it is optimal can flight mark.
Then judge whether track search tree completes traversal, there are two node ns3And n4It is not traversed, selects one of them
It is handled.As shown in Figure 10, node n3Corresponding to the solid line track in figure, the length of the track is 152.07, and currently most
It is excellent can flight track length be 146.31.Currently processed track length be longer than it is current it is optimal can flight track length, that
Just not no branch is necessary, directly gives up node n3。
Judge whether track search tree completes traversal again, is left node n4It is not traversed, now at it
Reason.As shown in figure 11, node n4Corresponding to the solid line track in figure, the length of the track is 166.74, same more optimal than current
Can flight mark length it is long, without continue branch, directly give up node n4。
So far, track search tree completes traversal, and planning terminates.As shown in figure 12, at this time it is current it is optimal can flight mark just
It is final optimal solution, the track indicated corresponding to dotted line in figure.
Claims (4)
1. a kind of real-time planing method of unmanned aerial vehicle flight path based on the path Dubins and branch and bound, it is characterised in that including following
Step:
Step 1: obtain the relevant information of unmanned plane during flying task, the digital elevation map including mission area, threaten quantity and
Distribution information, unmanned plane cruising altitude, unmanned plane initial position and target position, unmanned plane minimum turning radius, and it is right
Map is pre-processed.
Step 2: the root node of track search tree is arranged in initialization track search tree.Simultaneously according to agreement, set at this time most
It is short can flight mark length be infinity.
Step 3: judge whether track search tree completes traversal, if completing traversal, trajectory planning terminates, at the end of, if working as
It is preceding it is optimal can flight mark to remain as the length that initially sets up be infinitely great track, then planning failure is otherwise current optimal to fly
Track is exactly final optimal trajectory；If not completing traversal, according to certain strategy knot not traversed from track search tree
It selects one in point to be handled, actually not traversed node is equivalent to current live node.
Step 4: calculate the length L of track representated by the node of selection, judge L and it is current it is optimal can flight mark length Lmin
Size relation.If L is less than Lmin, 5 are thened follow the steps, otherwise returns to step 3.
Step 5: judging whether track representated by the node of selection with obstacle has collision.If there is collision, wherein some barrier is selected
Hinder round or obstacle group, carry out branch operation, generates two paths Dubins for bypassing obstacle to be processed, this two new tracks pair
It should be in two child nodes being extended on track search tree to current node.Then 3 are returned to step；If not touching
It hits, illustrates that track representated by the node has bypassed all obstacles, and again because the track is shorter in length than currently optimal fly
Track, so the node is actually superior to current optimal solution, then be arranged the track be it is new it is current it is optimal can flight mark, and calculate
Its length.Then 3 are returned to step.
2. a kind of unmanned aerial vehicle flight path based on the path Dubins and branch and bound according to claim 1 side of planning in real time
Method, it is characterised in that: the pretreated method of to map progress is in step 1, according to the cruising altitude of unmanned plane to digital elevation
Map is intercepted, and obtains two-dimensional map, and the irregular terrain profiles in two-dimensional map are carried out approximate substitution with a series of circle.
And by radius be less than unmanned plane turning radius circle amplify, until radius be not less than unmanned plane turning radius, two dimension is solved with this
The kinematics limitation of fixed-wing unmanned plane in plane.
3. a kind of unmanned aerial vehicle flight path based on the path Dubins and branch and bound according to claim 1 side of planning in real time
Method, it is characterised in that: in step 3 it is current it is optimal can flight mark length be the key that using Branch-and-Bound Algorithm, it is more early to find
One length it is shorter it is current it is optimal can flight mark, and use the length of the track as limit, it is empty can greatly to cut down search
Between, promote planning speed.It is selected in never traversed node in step 3 in the strategy and step 5 of node from colliding
Selected in obstacle some obstacle circle or obstacle group strategy, will affect find length it is shorter it is current it is optimal can flight mark speed
Degree.
4. a kind of unmanned aerial vehicle flight path based on the path Dubins and branch and bound according to claim 1 side of planning in real time
Method, it is characterised in that: after detecting collision in step 5, when generating two paths Dubins for bypassing obstacle to be processed, use
Make the method for geometry tangent line.
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110296698A (en) * | 2019-07-12 | 2019-10-01 | 贵州电网有限责任公司 | It is a kind of with laser scanning be constraint unmanned plane paths planning method |
| CN110297500A (en) * | 2019-06-28 | 2019-10-01 | 天津大学 | A kind of continuous path planning method giving unmanned plane under more way points |
| CN113050684A (en) * | 2021-03-06 | 2021-06-29 | 南京航空航天大学 | Emergency threat-oriented unmanned aerial vehicle track planning algorithm |
| CN113359853A (en) * | 2021-07-09 | 2021-09-07 | 中国人民解放军国防科技大学 | Route planning method and system for unmanned aerial vehicle formation cooperative target monitoring |
| CN113359840A (en) * | 2021-06-28 | 2021-09-07 | 中国人民解放军国防科技大学 | Rapid construction method and system for three-dimensional space flight path of unmanned aerial vehicle |
| CN113624237A (en) * | 2021-08-10 | 2021-11-09 | 北京理工大学 | Range-adjusting unmanned aerial vehicle track planning method based on Dubins path |
| CN114003059A (en) * | 2021-11-01 | 2022-02-01 | 河海大学常州校区 | UAV path planning method based on deep reinforcement learning under kinematic constraint condition |
| CN114442660A (en) * | 2021-12-31 | 2022-05-06 | 北京理工大学重庆创新中心 | Unmanned aerial vehicle searching method based on GPS and image |
| CN114964281A (en) * | 2021-12-20 | 2022-08-30 | 昆明理工大学 | Aircraft three-dimensional flight path planning method based on binary system gray wolf optimization algorithm |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103676944B (en) * | 2013-12-11 | 2016-11-23 | 北京理工大学 | The unmanned aerial vehicle flight path planing method searched for based on Dubins path and sparse A* |
| US20160371985A1 (en) * | 2015-06-16 | 2016-12-22 | Verizon Patent And Licensing Inc. | Dynamic navigation of uavs using three dimensional network coverage information |
| CN104516356B (en) * | 2015-01-08 | 2017-07-14 | 西北工业大学 | Dynamic disorder based on RRT evades algorithm |
-
2017
- 2017-10-19 CN CN201710973740.7A patent/CN109685237B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103676944B (en) * | 2013-12-11 | 2016-11-23 | 北京理工大学 | The unmanned aerial vehicle flight path planing method searched for based on Dubins path and sparse A* |
| CN104516356B (en) * | 2015-01-08 | 2017-07-14 | 西北工业大学 | Dynamic disorder based on RRT evades algorithm |
| US20160371985A1 (en) * | 2015-06-16 | 2016-12-22 | Verizon Patent And Licensing Inc. | Dynamic navigation of uavs using three dimensional network coverage information |
Non-Patent Citations (4)
| Title |
|---|
| 周红: ""射孔排炮设计优化算法的研究"", 《中国优秀硕士学位论文全文数据库工程科技Ⅰ辑》 * |
| 周绍磊等: ""无人机协同多任务分配的快速航路预估算法"", 《战术导弹技术》 * |
| 翁洁伟: ""多语言智能信息服务系统项目计划研究"", 《中国优秀硕士学位论文全文数据库经济与管理科学辑》 * |
| 蒲宏斌等: ""飞机低空突防轨迹优化研究"", 《飞行力学》 * |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110297500A (en) * | 2019-06-28 | 2019-10-01 | 天津大学 | A kind of continuous path planning method giving unmanned plane under more way points |
| CN110296698A (en) * | 2019-07-12 | 2019-10-01 | 贵州电网有限责任公司 | It is a kind of with laser scanning be constraint unmanned plane paths planning method |
| CN113050684A (en) * | 2021-03-06 | 2021-06-29 | 南京航空航天大学 | Emergency threat-oriented unmanned aerial vehicle track planning algorithm |
| CN113359840A (en) * | 2021-06-28 | 2021-09-07 | 中国人民解放军国防科技大学 | Rapid construction method and system for three-dimensional space flight path of unmanned aerial vehicle |
| CN113359853A (en) * | 2021-07-09 | 2021-09-07 | 中国人民解放军国防科技大学 | Route planning method and system for unmanned aerial vehicle formation cooperative target monitoring |
| CN113359853B (en) * | 2021-07-09 | 2022-07-19 | 中国人民解放军国防科技大学 | Route planning method and system for unmanned aerial vehicle formation cooperative target monitoring |
| CN113624237A (en) * | 2021-08-10 | 2021-11-09 | 北京理工大学 | Range-adjusting unmanned aerial vehicle track planning method based on Dubins path |
| CN113624237B (en) * | 2021-08-10 | 2024-01-02 | 北京理工大学 | Course adjustment unmanned aerial vehicle course planning method based on Dubin path |
| CN114003059A (en) * | 2021-11-01 | 2022-02-01 | 河海大学常州校区 | UAV path planning method based on deep reinforcement learning under kinematic constraint condition |
| CN114003059B (en) * | 2021-11-01 | 2024-04-16 | 河海大学常州校区 | UAV path planning method based on deep reinforcement learning under kinematic constraint condition |
| CN114964281A (en) * | 2021-12-20 | 2022-08-30 | 昆明理工大学 | Aircraft three-dimensional flight path planning method based on binary system gray wolf optimization algorithm |
| CN114442660A (en) * | 2021-12-31 | 2022-05-06 | 北京理工大学重庆创新中心 | Unmanned aerial vehicle searching method based on GPS and image |
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